We’re excited to release Federated Learning, the latest report and prototype from Cloudera Fast Forward Labs.
Federated learning makes it possible to build machine learning systems without direct access to training data. The data remains in its original location, which helps to ensure privacy and reduces communication costs. Read More
Training machine learning models often requires data from multiple parties. However, in some cases, data owners cannot share their data due to legal or privacy constraints but would still benefit from training a model jointly with multiple parties. Federated learning has arisen as an alternative to allow for the collaborative training of models without the sharing of raw data. However, attacks in the literature have demonstrated that simply maintaining data locally during training processes does not provide strong enough privacy guarantees. We need a federated learning system capable of preventing inference over the messages exchanged between parties during training as well as the final, trained model. Read More
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